2 citations · 7 across the 13 of their papers we have counts for
6 papers · 1 filter
Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario
Noam Fluss, Guy Hacohen, Daphna Weinshall
Semi-Supervised Learning (SSL) is a framework that utilizes both labeled and unlabeled data to enhance model performance. Conventional SSL methods operate under the assumption that…
Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering
Boaz Lerner, Guy Shiran, Daphna Weinshall
Recently, Semi-Supervised Learning (SSL) has shown much promise in leveraging unlabeled data while being provided with very few labels. In this paper, we show that ignoring the lab…
Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample
Itamar Winter, Daphna Weinshall
The generation of synthetic images is currently being dominated by Generative Adversarial Networks (GANs). Despite their outstanding success in generating realistic looking images,…
Multi-Modal Deep Clustering: Unsupervised Partitioning of Images
Guy Shiran, Daphna Weinshall
The clustering of unlabeled raw images is a daunting task, which has recently been approached with some success by deep learning methods. Here we propose an unsupervised clustering…
Blurred Images Lead to Bad Local Minima
Gal Katzhendler, Daphna Weinshall
Blurred Images Lead to Bad Local Minima
Novelty Detection in MultiClass Scenarios with Incomplete Set of Class Labels
Nomi Vinokurov, Daphna Weinshall
We address the problem of novelty detection in multiclass scenarios where some class labels are missing from the training set. Our method is based on the initial assignment of conf…